Many adaptive and learning control problems can be formulated as optimization problems aimed at minimizing objective functions related to system performance: In this context, several research directions emerge, such as establishing optimal control formulations for uncertain systems through Bellman’s principle of optimality, or addressing the “exploitation-exploration” dilemma, which involves weighing between controlling the system as well as possible based on current system knowledge (exploitation) and learning about system behavior through experimentation to control it better in the future (exploration).

Representative Topics

• Adaptive dynamic programming / Approximate dynamic programming
• Distributed learning and distributed optimization
• Cognitive adaptive control, adaptive optimal control
• Adaptive dual control

Representative Applications

• Self-optimizing energy-saving systems (e.g., energy systems)
• Self-optimizing formation (e.g., unmanned vehicles)
• Learning-based robotics

Representative Publications

• Yue D., Baldi S., Cao J., and De Schutter B., “Distributed adaptive optimization with weight-balancing”, IEEE Transactions on Automatic Control, scheduled for publication, 2021. doi:10.1109/TAC.2021.3071651
• Dai P., Yu W., Wang H., and Baldi S., “Distributed actor-critic algorithms for multi-agent reinforcement learning over directed graphs”, IEEE Transactions on Neural Networks and Learning Systems, scheduled for publication, 2022. doi:10.1109/TNNLS.2021.3139138
• Michailidis I., Baldi S., Kosmatopoulos E. B., and Ioannou P. A., “Adaptive optimal control for large-scale nonlinear systems”. IEEE Transactions on Automatic Control, Vol. 62(11), pp. 5567-5577, 2017. doi:10.1109/TAC.2017.2684458